DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea of speech/token without significantly more. The claims 1, 9 and 17 recite steps of accessing speech to be recognized (i.e., a data gathering step), providing the speech to a previously trained language model and receiving a first set of tokens therefrom (i.e., a data analysis/evaluation step), providing the speech to a custom language model and receiving a second set of tokens therefrom (i.e., a data analysis/evaluation step), determining a position within the speech where a token of the second set of tokens is a better fit than a token of the first set of tokens (i.e., a data analysis/evaluation step), and replacing a default word of the speech, determined by the previously trained language model, with a custom word at the position (i.e., a data analysis/evaluation step). This judicial exception is not integrated into a practical application because the claims are directed to an abstract idea with additional generic computer elements, where the generically recited computer elements (model, system, device, processor, memory, medium) do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because step “replacing a default word of the speech, determined by the previously trained language model, with a custom word at the position” corresponds to the well-understood, routine, conventional computer function of analyzing data as recognized by cited references Zhou and Choi (PTO 892 form).
The dependent claims also recite mental processes and do not add significantly more than the abstract idea and are as such similarly rejected.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
1. Claims 1, 4-9, 12-17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhou et al WO/2018/059957 A1 (“Zhou”)
Per claim 1, Zhou discloses a method, comprising:
accessing speech to be recognized (para. [0022]);
providing the speech to a previously trained language model and receiving a first set of tokens therefrom (the term "general-purpose speech recognition engine" refers to a type of speech recognition engine that is trained to recognize a broad range of speech from a natural human language such as English or Chinese. The general-purpose speech recognition engines generate speech recognition results based on a broad vocabulary of trained words and trained grammatical models that correspond to widely used speech patterns in a natural language …, para. [0014]; The process 200 continues as the system 100 generates a first plurality of candidate speech recognition results corresponding to the audio input data using a first general-purpose speech recognition engine based on the audio input data (block 208)…., para. [0032]; para. [0035]);
providing the speech to a custom language model and receiving a second set of tokens therefrom (The system 100 also generates a second plurality of the candidate speech recognition results using at least one domain-specific speech recognition engine (block 212)…., para. [0032]);
determining a position within the speech where a token of the second set of tokens is a better fit than a token of the first set of tokens (para. [0035]-[0036]); and
replacing a default word of the speech, determined by the previously trained language model, with a custom word at the position (fig. 4; para. [0036]).
Per claim 4, Zhou discloses the method of claim 1, wherein replacing the default word of the speech determined by the previously trained language model with the custom word at the position further comprises using string comparison metrics to select a best match of a set of custom words, comprising the custom word, to a word at the position (para. [0035]-[0036]).
Per claim 5, Zhou discloses the method of claim 1, wherein replacing the word with the custom word at the position further comprises providing the custom word as a portion of a transcription (fig. 4).
Per claim 6, Zhou discloses the method of claim 1, wherein replacing the default word with the custom word at the position further comprises providing the custom word as a portion of a command to a computing device (fig. 2, element 236; para. [0007]; para. [0022]; para. [0036]).
Per claim 7, Zhou discloses the method of claim 1, wherein the previously trained language model comprises at least one of a large language model or a neural network trained to recognize a generic set of words (para. [0014]; para. [0037]).
Per claim 8, Zhou discloses the method of claim 1, wherein at least one of the default word and the custom word comprise a plurality of words (para. [0025]; para. [0037]).
Per claim 9, Zhou discloses a system, comprising:
a computing device comprising one or more processors coupled to a computer memory comprising instructions (para. [0029]); and
wherein the instructions, when read by the one or more processors, cause the one or more processors to (para. [0021]) perform:
accessing speech to be recognized (para. [0022]);
providing the speech to a previously trained language model and receiving a first set of tokens therefrom (the term "general-purpose speech recognition engine" refers to a type of speech recognition engine that is trained to recognize a broad range of speech from a natural human language such as English or Chinese. The general-purpose speech recognition engines generate speech recognition results based on a broad vocabulary of trained words and trained grammatical models that correspond to widely used speech patterns in a natural language …, para. [0014]; The process 200 continues as the system 100 generates a first plurality of candidate speech recognition results corresponding to the audio input data using a first general-purpose speech recognition engine based on the audio input data (block 208)…., para. [0032]; para. [0035]);
providing the speech to a custom language model and receiving a second set of tokens therefrom (The system 100 also generates a second plurality of the candidate speech recognition results using at least one domain-specific speech recognition engine (block 212)…., para. [0032]);
determining a position within the speech where a token of the second set of tokens is a better fit than a token of the first set of tokens (para. [0035]-[0036]); and
replacing a default word of the speech, determined by the previously trained language model, with a custom word at the position (fig. 4; para. [0036]).
Per claim 12, Zhou discloses the system of claim 9, wherein replacing the default word of the speech determined by the previously trained language model with the custom word at the position further comprises using string comparison metrics to select a best match of a set of custom words, comprising the custom word, to a word at the position (para. [0035]-[0036]).
Per claim 13, Zhou discloses the system of claim 9, wherein replacing the default word with the custom word at the position further comprises providing the custom word as a portion of a transcription (fig. 4).
Per claim 14, Zhou discloses the system of claim 9, wherein replacing the default word with the custom word at the position further comprises providing the custom word as a portion of a command to a computing device (fig. 2, element 236; para. [0007]; para. [0022]; para. [0036]).
Per claim 15, Zhou discloses the system of claim 9, wherein the previously trained language model comprises at least one of a large language model or a neural network trained to recognize a generic set of words (para. [0014]; para. [0037]).
Per claim 16, Zhou discloses the system of claim 9, wherein at least one of the default word and the custom word comprise a plurality of words (para. [0025]; para. [0037]).
Per claim 17, Zhou discloses a non-transitory computer readable medium comprising instructions that, when read by a machine, cause the machine to perform:
accessing speech to be recognized (para. [0022]);
providing the speech to a previously trained language model and receiving a first set of tokens therefrom (the term "general-purpose speech recognition engine" refers to a type of speech recognition engine that is trained to recognize a broad range of speech from a natural human language such as English or Chinese. The general-purpose speech recognition engines generate speech recognition results based on a broad vocabulary of trained words and trained grammatical models that correspond to widely used speech patterns in a natural language …, para. [0014]; The process 200 continues as the system 100 generates a first plurality of candidate speech recognition results corresponding to the audio input data using a first general-purpose speech recognition engine based on the audio input data (block 208)…., para. [0032]; para. [0035]);
providing the speech to a custom language model and receiving a second set of tokens therefrom (The system 100 also generates a second plurality of the candidate speech recognition results using at least one domain-specific speech recognition engine (block 212)…., para. [0032]);
determining a position within the speech where a token of the second set of tokens is a better fit than a token of the first set of tokens (para. [0035]-[0036]); and
replacing a default word of the speech, determined by the previously trained language model, with a custom word at the position (fig. 4; para. [0036]).
Per claim 20, Zhou discloses the non-transitory computer readable medium of claim 17, further comprising instructions to cause the machine to perform replacing the default word of the speech determined by the previously trained language model with the custom word at the position, further comprising using string comparison metrics to select a best match of a set of custom words, comprising the custom word, to a word at the position (para. [0035]-[0036]).
Allowable Subject Matter
Claims 2, 3, 10, 11, 18 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUJIMI A ADESANYA whose telephone number is (571)270-3307. The examiner can normally be reached Monday-Friday 8:30-5:00pm.
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/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658
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